LAGAN: Deep Semi-Supervised Linguistic-Anthropology Classification with Conditional Generative Adversarial Neural Network
This work addresses the challenge of personalized education for ethnic minority groups in the post-communism era, but it appears incremental as it applies an existing GAN method to a new domain-specific dataset.
The paper tackles the problem of classifying linguistic ethnographic features in student engagement for ethnic minority education by formulating it as a semi-supervised problem and developing LA-GAN, a conditional deep generative adversarial network algorithm, with theoretical justification for its objective, regularization, and loss function.
Education is a right of all, however, every individual is different than others. Teachers in post-communism era discover inherent individualism to equally train all towards job market of fourth industrial revolution. We can consider scenario of ethnic minority education in academic practices. Ethnic minority group has grown in their own culture and would prefer to be taught in their native way. We have formulated such linguistic anthropology(how people learn)based engagement as semi-supervised problem. Then, we have developed an conditional deep generative adversarial network algorithm namely LA-GAN to classify linguistic ethnographic features in student engagement. Theoretical justification proves the objective, regularization and loss function of our semi-supervised adversarial model. Survey questions are prepared to reach some form of assumptions about z-generation and ethnic minority group, whose learning style, learning approach and preference are our main area of interest.